Teachers’ Attitudes and Practices on Differentiated Instruction
Bibliographic record
Abstract
This study investigated teachers’ attitudes and practices differentiating instruction for elementary school-aged students. This study looked at where, when and how it is being implemented, and the benefits and challenges using it. The extant literature revealed that the various components that make up differentiated instruction are based on sound research; however, there is a lack of research supporting full implementation. This qualitative study used semi-structured interviews with two experienced elementary school teachers, while using the descriptive coding process to analyze the data. Four themes emerged: leveling the playing field, which discusses the importance creating an equitable teaching experience for all. The role of assessment. This theme details how the importance of assessment in differentiation as well as some useful strategies teachers can use. How teachers address students’ needs, which looks at the methods teachers can use to differentiate instruction for students. Finally, going above and beyond talks about the amount of time and effort needed to differentiate effectively. Each of these themes support the notion that there is considerable learner variance amongst learners, and appropriate modifications and accommodations that meet these specific needs benefit their learning. However, considerable time and effort is needed to make this a reality. Not meeting these needs is an issue of equity teaching from a one-size-fits-all method of teaching benefits a few, but not all, students. It is recommended that the Ministry of Education take ownership of these implications by providing adequate professional training for teachers and administrators and investing into new technologies that can support teachers to differentiate instruction appropriately.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".